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Biowiki . Teaching . OldGradClassSyllabus

Biowiki . Teaching . OldGradClassSyllabus

Lecture topics

Approximate syllabus (subject to change)

  1. Computational basics: state-of-the-art workstations and webservers on a shoestring budget
  2. Probability basics
  3. An introduction to stochastic grammars for sequence analysis
  4. Information theory as applied to biological sequence analysis
  5. Discrete Markov chains: simple probabilistic models for DNA, RNA and protein sequences
  6. Applications of single-sequence HMMs to genome annotation
  7. Advanced topics in probability theory for Bayesian modeling
  8. Comparative genomics using pairwise Hidden Markov models ("Pair HMMs")
  9. Modeling evolution and other time-dependent processes using continuous-time Markov chains
  10. Modeling higher-order correlations in RNA (and proteins) using stochastic context-free grammars and graph grammars
  11. "Statistical Alignment": combining stochastic grammars and evolutionary models for massively parallel comparative genomics
  12. Probabilistic models in systems biology
  13. Advanced topics. May include:

Algorithms

The following algorithms for bioinformatics and systems biology are all implemented in DART and may be covered:

Note: Included topic Teaching.DartAlgorithms does not exist yet

Analysis of algorithms

NoisyChannelCodingTheorem, AlignmentAccuracy.

Extensions

Algorithms tackling the following subjects will be considered advanced/supplementary material to this class, and may be covered if there is time during the semester:

NeuralNetworks, GraphicalModels, MarkovRandomFields, ContextDependentSubstitution, CoalescentMethods; GaussianProcesses, BarashFriedmanClustering; PseudoknotPrediction, IsambertFoldingKinetics, MolecularForcefields, MembraneChannelDynamics.

RTreeSearch (for GFF).

Other stuff

GraduateClassFlier

-- IanHolmes - 22 Aug 2005


----- Revision r4 - 2007-06-18 - 21:25:02 - IanHolmes
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